


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_    ) ->      Union[str, Any]:







								'''simple docstring'''

								super().__init__()
								self.register_modules(unet=snake_case_			,							scheduler=snake_case_    )






					@torch.no_grad()
					def __call__(					self			,							snake_case_ = 1			,							snake_case_ = None			,							snake_case_ = 5_0			,							snake_case_ = "pil"			,							snake_case_ = True			,							**snake_case_			,							) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,							generator=snake_case_			,							)
								__lowercase			       = image.to(self.device    )

								# set step values
								self.scheduler.set_timesteps(snake_case_    )

								for t in self.progress_bar(self.scheduler.timesteps    ):
											# 1. predict noise model_output
											__lowercase			       = self.unet(snake_case_			,							snake_case_    ).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											__lowercase			       = self.scheduler.step(snake_case_			,							snake_case_			,							snake_case_    ).prev_sample

								__lowercase			       = (image / 2 + 0.5).clamp(0			,							1    )
								__lowercase			       = image.cpu().permute(0			,							2			,							3			,							1    ).numpy()
								if output_type == "pil":
											__lowercase			       = self.numpy_to_pil(snake_case_    )

								if not return_dict:
											return (image,), "This is a local test"

								return ImagePipelineOutput(images=snake_case_    ), "This is a local test"






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    snake_case = False   ,    snake_case = False   ,    snake_case = None   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(
						    features=snake_case   ,    cache_dir=snake_case   ,    keep_in_memory=snake_case   ,    streaming=snake_case   ,    num_proc=snake_case   ,    **snake_case   ,    )
						UpperCamelCase__       =							Generator(
						    cache_dir=snake_case   ,    features=snake_case   ,    generator=snake_case   ,    gen_kwargs=snake_case   ,    **snake_case   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.streaming:
									UpperCamelCase__       =							self.builder.as_streaming_dataset(split="train"  )
						# Build regular (map-style) dataset
						else:
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None

									self.builder.download_and_prepare(
									    download_config=snake_case   ,    download_mode=snake_case   ,    verification_mode=snake_case   ,    base_path=snake_case   ,    num_proc=self.num_proc   ,    )
									UpperCamelCase__       =							self.builder.as_dataset(
									    split="train"   ,    verification_mode=snake_case   ,    in_memory=self.keep_in_memory  )
						return dataset


